An economic evaluation of eptinezumab for the preventive treatment of migraine in the UK, with consideration for natural history and work productivity

Background Migraine is a highly prevalent neurological disease with a substantial societal burden due to lost productivity. From a societal perspective, we assessed the cost-effectiveness of eptinezumab for the preventive treatment of migraine. Methods An individual patient simulation of discrete competing events was developed to evaluate eptinezumab cost-effectiveness compared to best supportive care for adults in the United Kingdom with ≥ 4 migraine days per month and prior failure of ≥ 3 preventive migraine treatments. Individuals with sampled baseline characteristics were created to represent this population, which comprised dedicated episodic and chronic migraine subpopulations. Clinical efficacy, utility, and work productivity inputs were based on results from the DELIVER randomised controlled trial (NCT04418765). Timing of natural history events and treatment holidays—informed by the literature—were simulated to unmask any natural improvement of the disease unrelated to treatment. The primary outcomes were monthly migraine days, migraine-associated costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio, and net monetary benefit, each evaluated over a 5-year time horizon from 2020. Secondary analyses explored a lifetime horizon and an alternative treatment stopping rule. Results Treatment with eptinezumab resulted in an average of 0.231 QALYs gained at a saving of £4,894 over 5 years, making eptinezumab dominant over best supportive care (i.e., better health outcomes and less costly). This result was confirmed by the probabilistic analysis and all alternative assumption scenarios under the same societal perspective. Univariate testing of inputs showed net monetary benefit was most sensitive to the number of days of productivity loss, and monthly salary. Conclusions This economic evaluation shows that from a societal perspective, eptinezumab is a cost-effective treatment in patients with ≥ 4 migraine days per month and for whom ≥ 3 other preventive migraine treatments have failed. Trial registration N/A. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s10194-024-01749-8.


Supplemental Methods
Additional details of methods and input calculation are provided in this section.

Age at entry
The cumulative distribution function was constructed from TF3+ participants of DELIVER (all arms).This is depicted by the grey line and shown for comparison against a real-world source (orange and blue lines) (Figure S1).Age at entry was assumed to follow a normal distribution around the mean (SD) age of 45.2 (11.8) years.Mean (SD) age from the realworld source was similar at 46.5 (12.3) years.

Life expectancy
Gompertz distributions were fitted to recent Office for National Statistics data for life expectancy by gender (Figure S2).Simulated individuals entered the model with prespecified age of death; this was independent of other baseline characteristics besides gender.

MMDs at baseline
After simulated individuals were assigned to either EM or CM status, their MMD frequency was sampled.Beta distributions were fitted to data from the respective subpopulations of the TF3+ cohort of the DELIVER trial (dark blue and grey) (Figure S3).Mean (SD) MMDs were 9.8 (2.5) for EM and 19.9 (4.0) for CM.A beta distribution was also fitted to the composite data (yellow) and shown for reference next to the naive data (light blue).The dashed orange curve shows the larger TF3+ cohort.Sampling of baseline MMDs was independent of age and gender.

Other-cause discontinuation
Real-world evidence from subcutaneous anti-CGRP mAbs in people with chronic migraine in Sweden indicated that simple extrapolation of the short-term in-trial rate of adverse event discontinuation in DELIVER would likely underestimate the two-year rate of discontinuation for any reason.1Since reasons for discontinuation in the longer-run are likely broader than adverse events alone, an 'other-cause' rate was introduced into the model.This provided a means of calibrating the overall discontinuation rate to match the real-world source, as interpreted by expert clinical opinion, which was a diminishing risk up to two years of treatment (Figure S4).The determined exponential rate through years one and two was 0.3.

Discontinuation due to treatment emergent adverse events
All individuals were at risk of a TEAE leading to discontinuation during the eptinezumab assessment period, and Responders continued to be at risk thereafter.The rate of these event after assessment was based on the two-year open-label PREVAIL trial. 2 The observed rate was extrapolated using an exponential parametric function (Figure S5).The exponent of 0.03 was based on the 5.5% withdrawal rate at the end of the open-label study (2 years).

Natural history of migraine
Improvement in natural history was linked to age at established menopause (perimenopause may be linked with higher migraine frequency). 3Since 89% of modelled individuals were women, this concept was applied to all individuals and implemented through transformation (CM to EM) and resolution events (EM to residual level migraine frequency) (Figure S6).
Age at transformation was randomly sampled from a normal distribution of mean (SD) age 49.5 (5.0) years. 4Resolution followed a fixed period equating to the mean duration of menopause symptoms (4.3 years).

Work productivity and activity impairment
The effects of eptinezumab on self-reported work productivity in adults with migraine participating in the DELIVER trial has been reported elsewhere. 5However, the statistical relationship between absenteeism and presenteeism and migraine day frequency that was used in the model is presented in Table S1.The relationship between absenteeism hours (y) and MMDs (x) was linear, given by the equation  =  *  + , where b is the MMD coefficient and c is the intercept.For the eptinezumab strategy, the eptinezumab coefficient was added.
The relationship between presenteeism hours (y) and MMDs (x) was quadratic, given by the equation  = − *  2 +  *  + , where a is the MMDs^2 coefficient, b is the MMD coefficient, and c is the intercept.For the eptinezumab strategy, the eptinezumab coefficient was added.

Input parameters
Input point estimates are detailed along with the selected parameter distribution and standard error used for probabilistic analysis (Table S2).

Figure S1 .
Figure S1.Normal distribution for sampling age at model entry / commencement of

Figure S2 .
Figure S2.Gompertz distributions for sampling of life expectancy

Figure S3 .
Figure S3.Beta distributions for sampling MMDs at model entry

Figure S5 .
Figure S5.Exponential sampling distribution of time to treatment-emergent adverse

Figure S6 .
Figure S6.Age at menopause for sampling of age at transformation from CM to EM

Table S1 . Statistical description of work impairment on and off treatment
Abbreviations: ^2, squared; df, Degrees of freedom; MMDs, Monthly migraine days; SE, Standard error.